Image-to-Text
MLX
Safetensors
Transformers
qwen3_5
image-text-to-text
vision-language
vlm
document-understanding
structured-extraction
information-extraction
ocr
document-to-markdown
markdown
rag
reasoning
multilingual
conversational
8-bit precision
Instructions to use wearesage/nuextract3-4b-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use wearesage/nuextract3-4b-mlx-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir nuextract3-4b-mlx-8bit wearesage/nuextract3-4b-mlx-8bit
- Transformers
How to use wearesage/nuextract3-4b-mlx-8bit with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="wearesage/nuextract3-4b-mlx-8bit") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("wearesage/nuextract3-4b-mlx-8bit") model = AutoModelForImageTextToText.from_pretrained("wearesage/nuextract3-4b-mlx-8bit") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 991 Bytes
5b6fa6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | {
"image_processor": {
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Qwen3VLImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_pixels": 16777216,
"merge_size": 2,
"min_pixels": 65536,
"patch_size": 16,
"rescale_factor": 0.00392156862745098,
"temporal_patch_size": 2
},
"processor_class": "Qwen3VLProcessor",
"video_processor": {
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"fps": 2.0,
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"max_frames": 768,
"max_pixels": 786432,
"merge_size": 2,
"min_frames": 4,
"min_pixels": 131072,
"patch_size": 16,
"rescale_factor": 0.00392156862745098,
"temporal_patch_size": 2,
"video_processor_type": "Qwen3VLVideoProcessor"
}
}
|